Junjie Hu, Rheeya Uppaal, Apratim Dey, Yiting He, Yiqiao Zhong
We lifted 12 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Uppaal/detox-edit | canonical | 6 of 10 |
| uppaal/detox-edit | canonical | 1 of 1 |
| ziwei-zheng/nullu | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| get_continuation | Ran | uppaal/detox-edit/evaluation/win_rate.py code served (permissive licence) · get_code("fd8cb94482303177") |
| get_num_transformer_layers | Ran | Uppaal/detox-edit/utils/model_utils.py code served (permissive licence) · get_code("772f368569ca0f5d") |
| load_toxicity_preference | Ran | Uppaal/detox-edit/utils/dataset_utils.py code served (permissive licence) · get_code("ec4fdca101330482") |
| main | Ran | Uppaal/detox-edit/utils/startup.py code served (permissive licence) · get_code("cfb7124a6f05db32") |
| reduce_num_dps | Ran | Uppaal/detox-edit/utils/dataset_utils.py code served (permissive licence) · get_code("7bf4ed7f8521c691") |
| vector_matrix_cosine_similarity | Ran | Uppaal/detox-edit/utils/utils.py code served (permissive licence) · get_code("3dd8a319f62d60ec") |
| vector_vector_cosine_similarity | Ran | Uppaal/detox-edit/utils/utils.py code served (permissive licence) · get_code("aa5ffddfcd573d9a") |
| HalluEdit | Not yet run | ziwei-zheng/nullu/utils/halluedit.py code served (permissive licence) · get_code("6b9b938bdb4d6f5f") |
| get_current_gpu_use | Not yet run | Uppaal/detox-edit/utils/utils.py code served (permissive licence) · get_code("e61d0c538220694e") |
| get_model_category | Not yet run | Uppaal/detox-edit/utils/model_utils.py code served (permissive licence) · get_code("6387304a152872eb") |
| llm_judge_eval | Not yet run | Uppaal/detox-edit/evaluation/win_rate.py code served (permissive licence) · get_code("fe59b113554271a7") |
| load_large_model | Not yet run | Uppaal/detox-edit/utils/model_utils.py code served (permissive licence) · get_code("78599b638be017da") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recent alignment algorithms such as direct preference optimization (DPO) have been developed to improve the safety of large language models (LLMs) by training these models to match human behaviors exemplified by preference data. However, these methods are both computationally intensive and lacking in controllability and transparency, inhibiting their widespread use. Furthermore, these tuning-based methods require large-scale preference data for training and are susceptible to noise in this data. In this paper, we introduce a tuning-free alignment alternative, ProFS (Projection Filter for Subspaces), and demonstrate its effectiveness under the use case of toxicity reduction. Grounded on theory from factor analysis, ProFS is a sample-efficient model editing approach that identifies a toxic subspace in the model parameter space and reduces model toxicity by projecting away the detected toxic subspace. The toxic subspace is identified by extracting preference data embeddings from the language model, and removing non-toxic information from these embeddings. We show that ProFS is more sample-efficient than DPO, further showcasing greater robustness to noisy data. Finally, we attempt to connect tuning based alignment with editing, by establishing both theoretical and empirical connections between ProFS and DPO, showing that ProFS can be interpreted as a denoised version of a single DPO step. Our code is available at https: //github.com/Uppaal/detox-edit.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2405.13967")
get_code_for_paper("2405.13967")
have("2405.13967")
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